Data-Driven Application Maintenance: Views from the Trenches

In this paper we present our experience during design, development, and pilot deployments of a data-driven machine learning based application maintenance solution. We implemented a proof of concept to address a spectrum of interrelated problems encountered in application maintenance projects including duplicate incident ticket identification, assignee recommendation, theme mining, and mapping of incidents to business processes. In the context of IT services, these problems are frequently encountered, yet there is a gap in bringing automation and optimization. Despite long-standing research around mining and analysis of software repositories, such research outputs are not adopted well in practice due to the constraints these solutions impose on the users. We discuss need for designing pragmatic solutions with low barriers to adoption and addressing right level of complexity of problems with respect to underlying business constraints and nature of data.Comment: Earlier version of paper appearing in proceedings of the 4th International Workshop on Software Engineering Research and Industrial Practice (SER&IP), IEEE Press, pp. 48-54, 201

Usability discussions in open source development

The public nature of discussion in open source projects provides a valuable resource for understanding the mechanisms of open source software development. In this paper we explore how open source projects address issues of usability. We examine bug reports of several projects to characterise how developers address and resolve issues concerning user interfaces and interaction design. We discuss how bug reporting and discussion systems can be improved to better support bug reporters and open source developers

Exploring usability discussions in open source development

The public nature of discussion in open source projects provides a valuable resource for understanding the mechanisms of open source software development. In this paper we explore how open source projects address issues of usability. We examine bug reports of several projects to characterise how developers address and resolve issues concerning user interfaces and interaction design. We discuss how bug reporting and discussion systems can be improved to better support bug reporters and open source developers

Duplicate bug report detection using clustering

Bug reporting and fixing the reported bugs play a critical part in the development and maintenance of software systems. The software developers and end users can collaborate in this process to improve the reliability of software systems. Various end users report the defects they have found in the software and how these bugs affect them. However, the same defect may be reported independently by several users leading to a significant number of duplicate bug reports. There are a number of existing methods for detecting duplicate bug reports, but the best results so far account for only 24% of actual duplicates. In this paper, we propose a new method based on clustering to identify a larger proportion of duplicate bug reports while keeping the false positives of misidentified non-duplicates low. The proposed approach is experimentally evaluated on a large sample of bug reports from three public domain data sets. The results show that this approach achieves better performance in terms of a harmonic measure that combines true positive and true negative rates when compared to the existing methods